 Data transformations to improve the performance of health plan payment methodsSavannah L. Bergquist, Timothy Layton, Thomas G. McGuire and Sherri Rose Journal of Health Economics, 2019, vol. 66, issue C, 195-207 Abstract: The conventional method for developing health care plan payment systems uses observed data to study alternative algorithms and set incentives for the health care system. In this paper, we take a different approach and transform the input data rather than the algorithm, so that the data used reflect the desired spending levels rather than the observed spending levels. We present a general economic model that incorporates the previously overlooked two-way relationship between health plan payment and insurer actions. We then demonstrate our systematic approach for data transformations in two Medicare applications: underprovision of care for individuals with chronic illnesses and health care disparities by geographic income levels. Empirically comparing our method to two other common approaches shows that the “side effects” of these approaches vary by context, and that data transformation is an effective tool for addressing misallocations in individual health insurance markets. Keywords: Health insurance; Managed competition; Risk adjustment (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jhecon:v:66:y:2019:i:c:p:195-207 DOI: 10.1016/j.jhealeco.2019.05.005 Access Statistics for this article Journal of Health Economics is currently edited by J. P. Newhouse, A. J. Culyer, R. Frank, K. Claxton and T. McGuire More articles in Journal of Health Economics from Elsevier |
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